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AI",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_939100716":{"id":"flexible-column-container-e94d20fd41","propertiesId":"hub-hero","type":"2-column-even","alignColumns":"center","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-421baafcc7",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2":{"id":"title-v2-0d6eb53d55","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Agenti AI: guida all’architettura e alla governance dell’Agentic AI"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-25b29b1f59","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EGli agenti AI portano l’enterprise AI oltre i prompt isolati, integrandola in workflow capaci di ragionare, recuperare contesto, usare strumenti e intervenire. La sfida non consiste più solo nel creare agenti più capaci, ma nel collegarli a dati, app e sistemi di governance in modo affidabile per le aziende.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-902fdd671e",":type":"snowflake-site/components/container",":items":{"content_chip":{"id":"content-chip-e400ba9152","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/Laurie-Macpherson/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi la bio"},"image":{"id":"image","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a7e9fdff-6f08-4edc-9cd1-e213bb234aaa/laurie-macpherson.jpg?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Laurie MacPherson","isLcpImage":true,"width":"800",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Laurie MacPherson","Technical Writer, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip_copy":{"id":"content-chip-55314bf5b7","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/david-gaule/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi la bio"},"image":{"id":"image","height":"512","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fd9454ea-3b59-4d19-95be-534774cbd226/david.jpg?quality=85&preferwebp=true","lazyEnabled":true,"alt":"David Gaule","isLcpImage":false,"width":"512",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["David Gaule","Technical Editor, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip","content_chip_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["title_v2","text","container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-4417b05272",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-5cdc3e2a67","youtubeVideoId":"CtqKJV6gyGQ","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-579c804d6e","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container border-top","layout":"SIMPLE","id":"container-aaa9afc31f",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-421acb001d","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EApprofondimenti sugli agenti AI:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-3faa9eff7d","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/agent-evaluation/\"\u003EValutazione degli agenti AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/harness/\"\u003EFramework per agenti AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/agent-orchestration/\"\u003EOrchestrazione degli agenti AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/agent-planning/\"\u003EPianificazione degli agenti AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/autonomous-agents/\"\u003EAgenti autonomi\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/coding-agents/\"\u003ECoding agent\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-e725c346a6","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"callout__0":{"id":"text-dd38b215de","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EAGENTI AI: LA DEFINIZIONE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EUn agente AI è un sistema software che utilizza un modello AI all’interno di un workflow orchestrato per perseguire un obiettivo, interpretare il contesto, decidere i passaggi successivi, richiamare strumenti o fonti dati approvati, valutare i risultati e proseguire fino al completamento del task, alla sua escalation o all’interruzione.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-b4ea8f5067","text":"\u003Cp\u003EL’enterprise AI sta superando l’era dei prompt isolati. Solo un anno fa, molte iniziative di AI miravano a fornire al personale l’accesso ai \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-chatbots/\"\u003Echatbot\u003C/a\u003E: caricare un file, richiedere un riepilogo, porre una domanda. Oggi le organizzazioni vogliono sistemi di Agentic AI capaci di operare su dati, strumenti, codebase e workflow aziendali, con il contesto e il controllo necessari per completare task articolati in più passaggi.\u003C/p\u003E\r\n\u003Cp\u003ETuttavia, un agente AI non può essere trattato come un chatbot con più autorizzazioni. Deve accedere al contesto, ma non in modo illimitato a ogni sistema. Deve accedere agli strumenti, ma non essere libero di usare qualsiasi strumento in qualunque modo. Deve disporre dell’autonomia necessaria per svolgere un task, ma anche di controlli sufficienti affinché l’organizzazione possa verificare che cosa ha fatto, quali dati ha utilizzato e perché ha eseguito una determinata azione.\u003C/p\u003E\r\n\u003Cp\u003ESecondo Gartner, entro il 2028 il 33% delle app software aziendali includerà l’Agentic AI, un netto aumento rispetto a meno dell’1% nel 2024.\u003Csup\u003E1\u003C/sup\u003E Man mano che gli agenti entrano a far parte del livello software aziendale, la sfida pratica consiste nel collegarli a dati, strumenti e workflow tramite un livello di \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/\"\u003Egovernance\u003C/a\u003E capace di operare tra diversi fornitori, mantenere le autorizzazioni e garantire la verificabilità delle azioni.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-an-ai-agent":{"id":"title-v2-3508db2a7a","additionalClasses":"anchor-title anchor-title--what-is-an-ai-agent","type":"heading2","lines":["Cos’è un agente AI?"],":type":"snowflake-site/components/title-v2"},"text_what-is-an-ai-agent_0":{"id":"text-953f514910","text":"\u003Cp\u003EUn agente AI è un sistema software che utilizza l’AI per perseguire un obiettivo, prendere decisioni e intervenire tramite app o altri sistemi. Svolge un task interpretando prima la richiesta e raccogliendo il contesto, quindi decide il passaggio successivo, richiama gli strumenti appropriati, verifica i risultati e prosegue fino a ottenere un risultato o ad affidare il task a una persona o a un altro sistema.\u003C/p\u003E\n\u003Cp\u003ELa maggior parte degli agenti aziendali attuali utilizza \u003Ca href=\"https://www.snowflake.com/it/fundamentals/large-language-model/\"\u003Emodelli linguistici di grandi dimensioni\u003C/a\u003E (LLM) come livello di ragionamento. Un agente AI, tuttavia, è molto più del modello che utilizza. Comprende la logica di orchestrazione che circonda il modello: le istruzioni che ne definiscono il comportamento, il ciclo di pianificazione che determina il passaggio successivo, la memoria o lo stato del task a cui può fare riferimento, e i connettori che gli consentono di richiamare strumenti e fonti dati approvati.\u003C/p\u003E\n\u003Ch3\u003EAgentic AI vs. AI generativa vs. ML\u003C/h3\u003E\n\u003Cp\u003ESebbene il \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E (ML) tradizionale possa essere integrato nei workflow automatizzati e l’\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/generative-ai/\"\u003EAI generativa\u003C/a\u003E possa essere collegata a strumenti, nessuno dei due costituisce, da solo, un sistema di Agentic AI. A conferire a un sistema AI le caratteristiche dell’Agentic AI è il ciclo orientato a un obiettivo che circonda il modello: contesto, connettività, uso degli strumenti, stato e autonomia sufficiente per svolgere un task articolato in più passaggi.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EIl ML tradizionale esegue previsioni o classificazioni:\u003C/b\u003E I sistemi di machine learning vengono generalmente addestrati a riconoscere pattern nei dati e a restituire una previsione, una classificazione, un punteggio o una raccomandazione. L’output del modello può informare un workflow, ma il workflow stesso è definito al di fuori del modello.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EL’AI generativa crea nuovi contenuti:\u003C/b\u003E I modelli di AI generativa producono testo, immagini, codice, audio o altri output a partire da un prompt o un input. Una persona pone una domanda, richiede un riepilogo o fornisce istruzioni e il modello genera una risposta. L’interazione può essere conversazionale, ma in genere il sistema si limita a produrre un output per l’utente.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EL’Agentic AI agisce per raggiungere un obiettivo:\u003C/b\u003E L’Agentic AI utilizza modelli all’interno di un sistema più ampio, in grado di recuperare il contesto, scegliere i passaggi successivi, chiamare strumenti, osservare i risultati e proseguire fino a ottenere un risultato o affidare il task ad altri. Il modello può eseguire il ragionamento o la generazione, ma il sistema agentico gli fornisce l’accesso al contesto, un percorso per agire e un processo per decidere il passaggio successivo.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch3\u003EAgenti AI e agenti dati a confronto\u003C/h3\u003E\n\u003Cp\u003EUn agente dati è un agente AI specializzato nelle attività sui dati. Collega linguaggio naturale, contesto aziendale e sistemi di dati, consentendo di porre domande, generare query, esaminare metadati, verificare la qualità, riepilogare set di dati o analizzare anomalie senza passare manualmente da uno strumento all’altro.\u003C/p\u003E\n\u003Cp\u003EUn agente dati richiede un accesso governato ai dati. Deve comprendere le definizioni delle metriche, il livello di aggiornamento delle tabelle, il data lineage a livello di colonna, la proprietà, le policy di accesso e i percorsi approvati per le query. Ad esempio, se una persona richiede il tasso di abbandono dei clienti per area geografica, l’agente deve sapere quale definizione di abbandono è approvata, quale tabella dei clienti è aggiornata, se l’utente può visualizzare i dettagli a livello di riga e se la risposta deve essere aggregata.\u003C/p\u003E\n\u003Cp\u003E“Aggiungere contesto è oggi la sfida decisiva”, spiega William Allen, Head of Product for CoWork & Agents Snowflake. “La questione è come fornire contesto all’agente in ogni fase, senza sovraccaricarlo né indirizzarlo verso percorsi casuali”.\u003C/p\u003E\n\u003Cp\u003EGli agenti dati devono inoltre operare su tipi di dati diversi. Un’analisi dei ricavi può coinvolgere una tabella SQL, un contratto in formato PDF, la trascrizione di una conversazione con l’assistenza e la definizione di una dashboard. Un agente AI per i dati riunisce questi oggetti tramite un’interfaccia governata, consentendo al modello di ragionare sulla questione aziendale senza aggirare i controlli di accesso.\u003C/p\u003E\n\u003Cp\u003EPer questo motivo, l’adozione degli agenti AI nelle imprese richiede decisioni sull’architettura dati: la risposta dell’agente dipende dalla qualità, dall’aggiornamento, dalla semantica e dalle autorizzazioni dei dati sottostanti.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_what-is-an-ai-agent_0":{"id":"card-v2-8212a0b33d","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Customer story: Simon AI"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/case-study/simon-ai/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Leggi il case study"},"image":{"id":"image","height":"360","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--ae6471b2-bc6d-40a6-b4d4-9d207677d9ed/simon-ai-logo-nav.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Simon AI","isLcpImage":false,"width":"840",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ESimon AI utilizza Snowflake Cortex AI e Snowpark Container Services per supportare agenti AI componibili che consentono ai professionisti del marketing di offrire personalizzazione contestuale su vasta scala, senza spostare i dati né compromettere la governance. Gli agenti Simon operano direttamente nell’AI Data Cloud Snowflake e offrono ai professionisti del marketing un accesso governato e in tempo reale ai dati strutturati e non strutturati, consentendo ai clienti di sviluppare audience pertinenti al contesto il 90% più rapidamente e identificare oltre 100 nuove coorti di clienti ad alto valore.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_benefits-of-ai-agents":{"id":"title-v2-3dc238cd58","additionalClasses":"anchor-title anchor-title--benefits-of-ai-agents","type":"heading2","lines":["Vantaggi degli agenti AI"],":type":"snowflake-site/components/title-v2"},"text_benefits-of-ai-agents_0":{"id":"text-7edb654e14","text":"\u003Cp\u003ERispetto a un modello autonomo, il valore di un agente AI deriva dalla capacità di pianificare i passaggi, conservare o recuperare il contesto e utilizzare strumenti per agire su informazioni esterne al modello stesso.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003ERidurre l’orchestrazione manuale:\u003C/strong\u003E Non è necessario recuperare il contesto, incollarlo in un prompt, richiedere un riepilogo, copiare la risposta in un altro sistema, formulare un nuovo prompt quando manca qualcosa e verificare manualmente ogni passaggio. L’agente gestisce una parte maggiore del ciclo di recupero, ragionamento, azione e verifica all’interno del workflow.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAccorciare il percorso dalla domanda alla risposta basata sui dati:\u003C/strong\u003E Chi chiede perché i costi della pipeline siano aumentati non dovrebbe dover sapere quale tabella contiene lo storico di utilizzo, quale vista include la logica approvata per l’allocazione dei costi o quale responsabile della dashboard ha modificato il calcolo. Un agente dati governato può tradurre la domanda in task sui dati, recuperare il contesto corretto e presentare la risposta in linguaggio aziendale.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ERendere più coerenti i processi decisionali ripetitivi:\u003C/strong\u003E Il triage dei ticket, i controlli sulla qualità dei dati, la revisione dei log e i riepiloghi del rischio associato agli account richiedono l’applicazione dello stesso processo di ragionamento a grandi volumi di attività. La revisione umana rimane importante, soprattutto per le eccezioni, ma l’agente gestisce le attività ripetibili seguendo un percorso coerente.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAbilitare l’automazione adattiva:\u003C/strong\u003E Un workflow basato su regole si interrompe quando l’input cambia in modo imprevisto. Un agente può esaminare la nuova condizione, rivedere il piano e scegliere un altro percorso approvato. Questa adattabilità rende utili gli agenti e richiede che la governance operi vicino al livello di esecuzione.\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-agents-across-the-enterprise":{"id":"title-v2-4e1fc27c9b","additionalClasses":"anchor-title anchor-title--ai-agents-across-the-enterprise","type":"heading2","lines":["Agenti AI in tutta l’azienda"],":type":"snowflake-site/components/title-v2"},"text_ai-agents-across-the-enterprise_0":{"id":"text-496f45fd3a","text":"\u003Cp\u003EAgenti AI: i migliori casi iniziali non sono i più vistosi In genere si tratta di workflow con attività ripetitive, che richiedono molto contesto e coinvolgono più sistemi.\u003C/p\u003E\n\u003Ch3\u003EServizio clienti\u003C/h3\u003E\n\u003Cp\u003ENel servizio clienti e nell’assistenza, gli agenti AI eseguono il triage dei ticket, recuperano il contesto degli account, classificano i problemi, preparano le risposte e indirizzano le eccezioni alla revisione umana. L’agente AI non sostituisce il percorso di escalation, ma lo rende più selettivo gestendo la raccolta ripetitiva del contesto e la risoluzione preliminare.\u003C/p\u003E\n\u003Ch3\u003ESviluppo software\u003C/h3\u003E\n\u003Cp\u003ENello sviluppo software, i \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/coding-agents/\"\u003Ecoding agent\u003C/a\u003E pianificano, scrivono, testano e revisionano il codice nell’intera codebase. La differenza rispetto al completamento automatico risiede nell’ambito operativo. Un coding agent richiede il contesto del repository e delle issue, informazioni sulle dipendenze, risultati dei test e istruzioni specifiche per il progetto. Servono inoltre guardrail che definiscano quale codice può modificare, quali comandi può eseguire e quali azioni richiedono l’approvazione umana.\u003C/p\u003E\n\u003Ch3\u003EData operations\u003C/h3\u003E\n\u003Cp\u003ENelle operazioni sui dati, gli agenti AI monitorano le pipeline, esaminano i problemi di qualità dei dati, tracciano il data lineage e identificano gli asset downstream interessati. Un controllo di aggiornamento non riuscito diventa più utile quando l’agente AI può indicare quale tabella di origine è cambiata, quale trasformazione dipende da essa, quale dashboard è interessata e chi è responsabile del workflow.\u003C/p\u003E\n\u003Ch3\u003ESales operations\u003C/h3\u003E\n\u003Cp\u003ENelle attività commerciali e di revenue generation, gli agenti AI sintetizzano i dati dei sistemi di gestione delle relazioni con i clienti (CRM), le trascrizioni delle chiamate, la cronologia degli account e l’utilizzo dei prodotti per segnalare gli account a rischio o preparare comunicazioni. L’output utile non è un riepilogo generico dell’account. È una raccomandazione basata sul contesto attuale del cliente, con i segnali di origine visibili.\u003C/p\u003E\n\u003Ch3\u003EOperazioni IT e di sicurezza\u003C/h3\u003E\n\u003Cp\u003ENelle operazioni IT e di sicurezza, gli agenti AI esaminano i log, correlano gli avvisi, recuperano il contesto degli asset e supportano la valutazione iniziale degli incidenti. Questi workflow richiedono una governance rigorosa perché l’agente AI può interagire con sistemi sensibili, dati telemetrici di sicurezza e azioni ad alto impatto. Le implementazioni iniziali più sicure mantengono spesso l’approvazione umana, utilizzando gli agenti AI per ridurre il tempo necessario a raccogliere il contesto.\u003C/p\u003E\n\u003Cp\u003E\u003Ci\u003EGuarda Andrew Ng, uno dei principali ricercatori nel campo dell’AI, analizzare l’ascesa degli agenti AI e del ragionamento agentico:\u003C/i\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_ai-agents-across-the-enterprise_0":{"id":"embed-1bcead3139","youtubeVideoId":"KrRD7r7y7NY","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_how-do-ai-agents-work":{"id":"title-v2-87f52f80f4","additionalClasses":"anchor-title anchor-title--how-do-ai-agents-work","type":"heading2","lines":["Come funzionano gli agenti AI?"],":type":"snowflake-site/components/title-v2"},"text_how-do-ai-agents-work_0":{"id":"text-20960ca139","text":"\u003Cp\u003ELa maggior parte degli agenti AI segue un ciclo: percepire, ragionare, pianificare, coordinare, agire e migliorare. Le denominazioni variano a seconda del framework, ma la sequenza descrive lo stesso modello operativo.\u003C/p\u003E\r\n\u003Ch3\u003EPercepire\u003C/h3\u003E\r\n\u003Cp\u003EL’agente AI inizia percependo l’ambiente. Ciò può significare leggere il prompt di un utente, recuperare un documento, esaminare una tabella, ricevere un evento da un sistema di workflow o osservare l’output di una chiamata a uno strumento. L’input raramente consiste in una sola frase; nei workflow aziendali include spesso metadati, autorizzazioni, contesto delle fonti, regole di business e stato precedente dell’attività.\u003C/p\u003E\r\n\u003Ch3\u003ERagionare\u003C/h3\u003E\r\n\u003Cp\u003EL’agente AI ragiona quindi sull’obiettivo e sul contesto disponibile. Stabilisce quali informazioni sono rilevanti, quali mancano e quali vincoli si applicano. Per un agente AI per i dati, ciò può includere il significato di una metrica, il livello di aggiornamento di una tabella, i privilegi di accesso dell’utente e la necessità di unire dati sensibili per fornire la risposta richiesta.\u003C/p\u003E\r\n\u003Ch3\u003EPianificare\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/agent-planning/\"\u003ELa pianificazione\u003C/a\u003E trasforma il ragionamento in un percorso operativo. L’agente AI stabilisce quali passaggi eseguire, quali strumenti chiamare e in quale ordine procedere. In un workflow fisso, il percorso può essere predefinito. In un workflow più autonomo, l’agente AI sceglie il passaggio successivo in base a ciò che ha già osservato.\u003C/p\u003E\r\n\u003Ch3\u003ECoordinare\u003C/h3\u003E\r\n\u003Cp\u003EIl coordinamento diventa importante quando sono coinvolti più strumenti, agenti AI o passaggi di approvazione. Un agente AI può recuperare i documenti pertinenti mentre un altro verifica i vincoli delle policy. Un agente AI supervisore può delegare attività secondarie, confrontare i risultati e stabilire se una persona debba approvare l’azione successiva.\u003C/p\u003E\r\n\u003Ch3\u003EAgire\u003C/h3\u003E\r\n\u003Cp\u003EL’agente AI passa quindi all’azione. Chiama una funzione, esegue query sui dati, aggiorna un ticket, invia un messaggio, prepara codice, attiva un workflow o restituisce una risposta. Negli ambienti governati, l’azione deve essere registrata con dettagli sufficienti per ricostruire quanto accaduto: la richiesta, i dati consultati, lo strumento utilizzato, l’output restituito e il percorso decisionale seguito.\u003C/p\u003E\r\n\u003Ch3\u003EMigliorare\u003C/h3\u003E\r\n\u003Cp\u003EInfine, l’agente AI apprende in senso operativo: integra il feedback, aggiorna lo stato dell’attività, archivia informazioni approvate in memoria o adatta i passaggi successivi. Questo apprendimento non implica necessariamente un nuovo addestramento del foundation model. Più spesso, consiste nel migliorare il sistema che circonda il modello attraverso aggiornamenti della memoria, \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/agent-evaluation/\"\u003Erisultati delle valutazioni\u003C/a\u003E, modifiche ai prompt, tuning delle policy o un instradamento più efficace verso gli strumenti.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_reasoning-and-action-paradigms":{"id":"title-v2-69562557cf","additionalClasses":"anchor-title anchor-title--reasoning-and-action-paradigms","type":"heading2","lines":["Paradigmi di ragionamento e azione"],":type":"snowflake-site/components/title-v2"},"text_reasoning-and-action-paradigms_0":{"id":"text-47aca51eec","text":"\u003Cp\u003EGli agenti AI possono essere configurati per ragionare e agire in modi diversi. Alcuni workflow chiedono al modello di analizzare un problema prima di rispondere. Altri consentono all’agente AI di interagire con gli strumenti, osservare i risultati e rivedere il passaggio successivo. Il modello più adatto dipende dalla prevedibilità dell’attività, dalla quantità di feedback fornita dall’ambiente e dal livello di controllo che l’organizzazione deve esercitare sul workflow.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ERagionamento passo-passo.\u003C/b\u003E I primi approcci alla progettazione degli agenti AI si basavano su tecniche di prompting sviluppate per gli LLM. Un concetto precursore era il ragionamento passo dopo passo, nel quale il modello riceve un prompt che gli chiede di scomporre un problema in passaggi intermedi prima di produrre una risposta. In questa configurazione, il ragionamento precede la risposta o l’azione finale. Il modello non modifica il proprio percorso in base al feedback esterno, a meno che il workflow non preveda un’altra iterazione.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EReAct.\u003C/b\u003E ReAct, abbreviazione di reasoning and action, modifica la sequenza. Anziché ragionare una sola volta all’inizio, l’agente AI alterna ragionamento, azione e osservazione. Può decidere di interrogare una tabella, esaminare il risultato, rilevare che la definizione della metrica è ambigua, recuperare i metadati e quindi modificare il passaggio successivo. La differenza rispetto al chain-of-thought non consiste semplicemente in una “maggiore capacità di ragionamento”, ma in un ragionamento che cambia dopo l’interazione dell’agente AI con uno strumento o un ambiente.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ERiflessione.\u003C/b\u003E La riflessione aggiunge un ulteriore controllo. L’agente AI esamina la propria risposta, il piano o il risultato di uno strumento prima di finalizzare l’output o proseguire il workflow. Un coding agent può generare un test, eseguirlo, esaminare l’errore e valutare criticamente il codice che ha scritto. Un agente AI per i dati può generare una query SQL, confrontare il risultato con la richiesta dell’utente e rilevare che la query applica un filtro sulla data di prenotazione, mentre la definizione della metrica richiede la data di transazione.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAgenti basati sulla pianificazione e agenti reattivi.\u003C/b\u003E Gli agenti basati sulla pianificazione e quelli reattivi rappresentano un’ulteriore distinzione. Un agente basato sulla pianificazione crea un piano articolato in più passaggi prima di agire, spesso con checkpoint e dipendenze. Un agente reattivo sceglie un passaggio alla volta in base all’evoluzione delle condizioni. Il modulo di pianificazione definito nell’architettura determina il livello di struttura disponibile prima che l’agente AI inizi ad agire e il grado di libertà con cui può modificare il percorso.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPrompt chaining.\u003C/b\u003E Il prompt chaining scompone una task in una sequenza di prompt più piccoli. Un prompt estrae le entità, quello successivo recupera il contesto, il seguente riepiloga i risultati e l’ultimo formatta la risposta finale. Il prompt chaining è generalmente più rigido e sequenziale rispetto a ReAct. È utile quando il workflow è noto in anticipo e ogni passaggio alimenta quello successivo.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELa maggior parte degli agenti AI in produzione utilizza una combinazione di questi paradigmi. Un workflow può iniziare con un piano, utilizzare ReAct per raccogliere le informazioni mancanti, applicare la riflessione prima di generare un output ad alto rischio e ricorrere all’invocazione strutturata degli strumenti per chiamare funzioni, API e nodi di workflow approvati oppure sistemi connessi tramite \u003Ca href=\"https://modelcontextprotocol.io/docs/getting-started/intro\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EModel Context Protocol\u003C/a\u003E (MCP). La questione progettuale non è quale paradigma sia migliore, ma quanta flessibilità, feedback e controllo richieda il task.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EParadigma\u003C/th\u003E\r\n\u003Cth\u003EIdeale per\u003C/th\u003E\r\n\u003Cth\u003ETrade-off principale\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ERagionamento passo-passo\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EProblemi che richiedono una scomposizione prima di formulare una risposta\u003C/td\u003E\r\n\u003Ctd\u003ECapacità limitata di modificare il percorso senza un’altra iterazione del workflow\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EReAct\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ETask in cui i risultati degli strumenti devono determinare il passaggio successivo\u003C/td\u003E\r\n\u003Ctd\u003ECosti, latenza e complessità di esecuzione maggiori\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ERiflessione\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EOutput che richiedono una revisione prima del completamento\u003C/td\u003E\r\n\u003Ctd\u003EAggiunge un altro passaggio di elaborazione del modello o di valutazione\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EAgenti basati sulla pianificazione\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EWorkflow con dipendenze o passaggi soggetti ad approvazione\u003C/td\u003E\r\n\u003Ctd\u003EMinore flessibilità quando le condizioni cambiano in modo imprevisto\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EAgenti reattivi\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EWorkflow dinamici in cui l’azione successiva dipende da nuove osservazioni\u003C/td\u003E\r\n\u003Ctd\u003EAudit potenzialmente più complesso senza solide funzionalità di tracciamento\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EPrompt chaining\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EWorkflow noti e ripetibili\u003C/td\u003E\r\n\u003Ctd\u003EMinore adattabilità rispetto ai cicli aperti degli agenti AI\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-are-the-types-of-ai-agents":{"id":"title-v2-b3a653b9b8","additionalClasses":"anchor-title anchor-title--what-are-the-types-of-ai-agents","type":"heading2","lines":["Quali sono i tipi di agenti AI?"],":type":"snowflake-site/components/title-v2"},"text_what-are-the-types-of-ai-agents_0":{"id":"text-670e62fb49","text":"\u003Cp\u003EGli agenti AI sono spesso classificati in cinque tipi canonici: agenti reattivi semplici, agenti reattivi basati su modello, agenti basati sugli obiettivi, agenti basati sull’utilità e agenti con capacità di apprendimento. Queste categorie derivano dalle prime ricerche sull’AI, ma aiutano ancora a comprendere le scelte progettuali alla base dei moderni sistemi di agenti AI.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EUn agente reattivo semplice\u003C/strong\u003E risponde allo stato corrente applicando regole predefinite. Se la condizione è vera, esegue l’azione corrispondente. Questa architettura è adatta a task circoscritti in condizioni stabili, ma non mantiene un modello del mondo né considera le conseguenze future.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EUn agente reattivo basato su modello\u003C/strong\u003E mantiene una rappresentazione interna dell’ambiente. Anziché reagire solo all’input corrente, utilizza lo stato memorizzato per determinare che cosa sta accadendo. Ad esempio, un agente operativo può monitorare l’ultimo stato noto di una pipeline, la modifica più recente allo schema e il proprietario associato alla tabella interessata prima di decidere quale avviso generare.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EUn agente basato su obiettivi\u003C/strong\u003E valuta le azioni rispetto al risultato desiderato. Non si limita a verificare le condizioni, ma sceglie anche le azioni che avvicinano il sistema a un obiettivo. Un agente del servizio clienti che deve risolvere una contestazione sulla fatturazione può raccogliere la cronologia dell’account, confrontare le regole delle policy, preparare una risposta e inoltrare il caso a un livello superiore solo quando il task supera la sua autorità.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EUn agente basato sull’utilità\u003C/strong\u003E valuta diversi risultati. Può confrontare velocità, costi, livello di confidenza, rischio e valore aziendale prima di selezionare un’azione. In un contesto aziendale, l’utilità è importante quando la risposta più rapida non è necessariamente la più sicura o quando un percorso di ragionamento più costoso è giustificato solo per decisioni ad alto rischio.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EUn agente con capacità di apprendimento\u003C/strong\u003E migliora attraverso il feedback. Il feedback può provenire da utenti, valutatori, sistemi di monitoraggio o risultati di task precedenti. Negli ambienti di produzione, il miglioramento avviene spesso aggiornando prompt, memoria, logica di routing, regole delle policy e set di valutazione, anziché riaddestrando continuamente il modello sottostante.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EDue tipi avanzati di agenti sono particolarmente rilevanti per i workflow aziendali:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cstrong\u003EGli agenti gerarchici\u003C/strong\u003E suddividono il lavoro su più livelli: un agente supervisore delega i task secondari ad agenti specializzati.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EI sistemi multi-agente\u003C/strong\u003E coordinano più agenti che possono operare in sequenza, in parallelo o sotto un orchestratore condiviso.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EIn pratica, molte implementazioni aziendali classificano gli agenti anche in base all’autonomia: gli agenti completamente autonomi eseguono task con un intervento umano limitato, mentre quelli semi-autonomi richiedono l’approvazione prima di intraprendere azioni ad alto rischio. Gli agenti specifici per dominio restringono ulteriormente l’ambito operativo, come un agente per i dati, un coding agent, un agente di sicurezza o un agente per le sales operations.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-agent-architecture-core-components":{"id":"title-v2-5f0b0e97a5","additionalClasses":"anchor-title anchor-title--ai-agent-architecture-core-components","type":"heading2","lines":["Architettura degli agenti AI: componenti principali"],":type":"snowflake-site/components/title-v2"},"text_ai-agent-architecture-core-components_0":{"id":"text-7cdc183463","text":"\u003Cp\u003EOltre a un foundation model, un agente AI richiede anche un livello di orchestrazione, un modulo di pianificazione, un’architettura della memoria, integrazioni con gli strumenti e un piano di controllo che governi l’esecuzione.\u003C/p\u003E\n\u003Ch3\u003EFoundation model\u003C/h3\u003E\n\u003Cp\u003EIl foundation model offre capacità flessibili di interpretazione, ragionamento e generazione. Molti sistemi attuali utilizzano LLM, anche se gli agenti non devono necessariamente limitarsi ai modelli linguistici. Il modello fornisce capacità di ragionamento flessibili, mentre l’architettura circostante determina quali informazioni l’agente può conoscere, quali strumenti può utilizzare e che cosa accade quando il task cambia.\u003C/p\u003E\n\u003Cp\u003ELa scelta del modello deve rimanere flessibile. Quando le aziende sviluppano agenti per workflow diversi, vincolare ogni caso d’uso a un unico foundation model può limitare le prestazioni, il controllo dei costi e l’adattabilità. “Se ci si vincola a un solo fornitore, nel giro di pochi mesi si potrebbero incontrare difficoltà”, afferma Allen. “Un altro fornitore potrebbe emergere all’improvviso con una nuova innovazione straordinaria e si rischierebbe di perdere l’opportunità. Riteniamo che la possibilità di scegliere il routing dei modelli sia fondamentale”.\u003C/p\u003E\n\u003Ch3\u003ELivello di orchestrazione\u003C/h3\u003E\n\u003Cp\u003EIl livello di orchestrazione gestisce il ciclo dell’agente. Trasmette il contesto al modello, instrada le chiamate agli strumenti, memorizza lo stato intermedio, gestisce i nuovi tentativi e determina se il workflow deve continuare, interrompersi, passare a un livello superiore o essere trasferito a un altro agente.\u003C/p\u003E\n\u003Ch3\u003EModulo di pianificazione\u003C/h3\u003E\n\u003Cp\u003EIl modulo di pianificazione suddivide un obiettivo in passaggi più piccoli. Per una richiesta come “individua il motivo del calo dei ricavi nella regione nord-orientale”, l’agente può pianificare di identificare la definizione della metrica pertinente, interrogare i ricavi recenti per segmento, confrontarli con i periodi precedenti, verificare l’aggiornamento della pipeline e controllare se una tabella di origine è cambiata. In un agente più semplice, la pianificazione può procedere un passaggio alla volta. In un agente più complesso, l’agente crea in anticipo un piano articolato in più passaggi e lo rivede man mano che riceve i risultati degli strumenti.\u003C/p\u003E\n\u003Ch3\u003EArchitettura della memoria\u003C/h3\u003E\n\u003Cp\u003ELa memoria garantisce continuità all’agente. La memoria a breve termine conserva lo stato immediato del task: la richiesta dell’utente, gli output degli strumenti, i vincoli, i documenti recuperati e i turni precedenti della conversazione. La memoria a lungo termine archivia conoscenze riutilizzabili, come preferenze degli utenti, decisioni precedenti, definizioni specifiche dell’organizzazione o problemi risolti. L’architettura della memoria determina che cosa viene archiviato, come viene recuperato, per quanto tempo persiste e quali parti della memoria sono disponibili per ciascun agente.\u003C/p\u003E\n\u003Ch3\u003EIntegrazione degli strumenti\u003C/h3\u003E\n\u003Cp\u003EL’integrazione degli strumenti collega l’agente ai sistemi in cui viene svolto il lavoro. Questi strumenti possono includere database, indici di ricerca, repository di codice, sistemi di ticketing, app CRM, motori di workflow, archivi documentali o API interne. La chiamata di funzioni offre al modello un metodo strutturato per richiamare questi strumenti, mentre standard come MCP stanno emergendo per uniformare il modo in cui gli agenti individuano e utilizzano funzionalità esterne.\u003C/p\u003E\n\u003Ch3\u003EPiano di controllo\u003C/h3\u003E\n\u003Cp\u003EIl piano di controllo è il livello governato della piattaforma che collega dati, modelli, strumenti e workflow aziendali, applicando le autorizzazioni, instradando le azioni, registrando le attività e gestendo il funzionamento degli agenti nei diversi sistemi. Definisce inoltre i limiti di costo e latenza e gestisce le approvazioni umane. Anche quando un agente opera su dati governati all’interno di una piattaforma come Snowflake, i contenuti recuperati, i prompt degli utenti e gli output degli strumenti devono essere trattati come input di un workflow agentico, non come istruzioni che il modello può seguire alla cieca.\u003C/p\u003E\n\u003Cp\u003EPer gli agenti di enterprise AI, questo livello non è un componente infrastrutturale opzionale. È ciò che trasforma un workflow basato su modelli in un sistema che un’organizzazione può governare con fiducia.\u003C/p\u003E\n\u003Ch3\u003EFramework di valutazione e ottimizzazione\u003C/h3\u003E\n\u003Cp\u003EUn framework di valutazione e ottimizzazione verifica le prestazioni di un agente AI su task reali, individua i punti critici e consente di migliorare il sistema senza ricorrere a interventi di tuning manuali una tantum. Anziché giudicare l’agente AI solo dalla risposta finale, il \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/harness/\"\u003Eframework\u003C/a\u003E (agent harness) può valutare il piano, il contesto recuperato, le chiamate agli strumenti, gli output intermedi, i costi, la latenza e il risultato finale.\u003C/p\u003E\n\u003Cp\u003ENelle implementazioni più avanzate, il framework può anche proporre modifiche alle istruzioni, agli strumenti, ai modelli semantici, ai prompt o alla configurazione del workflow dell’agente AI, quindi sottoporle a set di regressione e valutazione prima di promuoverle. Il miglioramento dell’agente AI diventa così un ciclo governato: valutare, diagnosticare, modificare, testare e conservare solo i cambiamenti che migliorano le prestazioni senza compromettere comportamenti che in precedenza funzionavano correttamente.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_multi-agent-systems-and-orchestration":{"id":"title-v2-09b7fa6166","additionalClasses":"anchor-title anchor-title--multi-agent-systems-and-orchestration","type":"heading2","lines":["Sistemi multi-agente e orchestrazione"],":type":"snowflake-site/components/title-v2"},"text_multi-agent-systems-and-orchestration_0":{"id":"text-c825de4fa5","text":"\u003Cp\u003EUn sistema multi‑agente utilizza più agenti AI per completare un task. Il valore risiede nella specializzazione: un agente AI recupera il contesto, un altro analizza le policy, un altro scrive il codice, un altro convalida il risultato e un orchestratore coordina il workflow.\u003C/p\u003E\n\u003Cp\u003EQuesta specializzazione è spesso il motivo principale per cui si adotta un sistema multi‑agente. Anziché affidare ogni funzione a un unico agente AI generalista, è possibile suddividere il lavoro tra agenti più specializzati, con responsabilità più chiare.\u003C/p\u003E\n\u003Cp\u003EIl trade-off è l’overhead di coordinamento. Ogni passaggio di consegne richiede stato, autorizzazioni, gestione degli errori e una registrazione chiara delle operazioni svolte da ciascun agente AI. In pratica, la progettazione multi‑agente non consiste tanto nell’aggiungere più agenti AI, quanto nel decidere dove la specializzazione migliori il risultato abbastanza da giustificare la complessità aggiuntiva.\u003C/p\u003E\n\u003Ch3\u003EOrchestrazione sequenziale\u003C/h3\u003E\n\u003Cp\u003EL’orchestrazione sequenziale funziona come una pipeline. Un agente AI completa una fase e ne passa l’output al successivo. È l’equivalente multi‑agente del prompt chaining e funziona bene quando l’ordine dei task è prevedibile.\u003C/p\u003E\n\u003Cp\u003EAd esempio, un workflow di elaborazione documentale può estrarre entità, classificare il documento, verificare le regole di compliance e redigere una sintesi secondo una sequenza fissa. Ogni fase dipende dall’output di quella precedente, quindi il workflow può essere esaminato come una catena di decisioni intermedie.\u003C/p\u003E\n\u003Ch3\u003EOrchestrazione gerarchica\u003C/h3\u003E\n\u003Cp\u003EL’orchestrazione gerarchica utilizza un agente AI supervisore. Il supervisore riceve l’obiettivo, delega i task secondari ad agenti AI specializzati e compone il risultato finale.\u003C/p\u003E\n\u003Cp\u003EFramework come LangGraph supportano questo tipo di workflow di produzione perché i modelli di stato basati su grafi si adattano bene a checkpoint, tentativi ripetuti e punti di ripristino. I framework basati sui ruoli, come CrewAI, sono spesso utilizzati per prototipi nei quali si vogliono assegnare rapidamente task ad agenti AI identificati per nome prima di consolidare il workflow.\u003C/p\u003E\n\u003Ch3\u003EOrchestrazione parallela\u003C/h3\u003E\n\u003Cp\u003EL’orchestrazione parallela invia lo stesso problema contemporaneamente a più agenti AI, quindi unisce o confronta gli output. Oggi questo modello è meno comune negli ambienti di produzione perché aumenta i costi, la latenza e la complessità della riconciliazione.\u003C/p\u003E\n\u003Cp\u003EResta comunque utile nei workflow con un’intensa attività di valutazione, nei quali risposte indipendenti aumentano il livello di affidabilità. Ad esempio, più agenti AI possono esaminare la stessa risposta proposta, verificare ipotesi diverse o confrontare il risultato con fonti distinte prima di comporre la risposta finale.\u003C/p\u003E\n\u003Ch3\u003EStandard per la comunicazione tra strumenti e agenti AI\u003C/h3\u003E\n\u003Cp\u003EMCP e i protocolli emergenti di comunicazione tra agenti AI fanno parte di questo livello di orchestrazione. MCP standardizza il modo in cui gli agenti AI e le app di AI si connettono a strumenti e fonti dati. I protocolli di comunicazione tra agenti AI definiscono come gli agenti si scambiano messaggi, stato e output dei task.\u003C/p\u003E\n\u003Cp\u003EQuesti standard consentono di stabilire se e come l’organizzazione può governare quale agente AI ha chiamato un determinato strumento, con le autorizzazioni di chi, per quale task e con quale risultato.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_agent-autonomy-and-guardrails":{"id":"title-v2-eefe7ba286","additionalClasses":"anchor-title anchor-title--agent-autonomy-and-guardrails","type":"heading2","lines":["Autonomia e guardrail degli agenti AI"],":type":"snowflake-site/components/title-v2"},"text_agent-autonomy-and-guardrails_0":{"id":"text-5e345db305","text":"\u003Cp\u003EL’autonomia si colloca lungo uno spettro. Un agente AI può operare autonomamente in una parte del workflow ed essere soggetto a vincoli rigorosi in un’altra. Può recuperare documenti, esaminare metadati e formulare autonomamente una raccomandazione, ma richiedere l’approvazione umana prima di aggiornare un sistema, inviare un messaggio o modificare una pipeline. L’equilibrio varia anche in base al caso d’uso.\u003C/p\u003E\n\u003Cp\u003EIl livello di guardrail necessario per un workflow dipende sia dalle conseguenze di un errore, ovvero cosa accade se l’agente AI sbaglia e quanto è difficile annullarne gli effetti, sia dal punto in cui è più probabile che l’errore si verifichi. Una regola di approvazione generale può ridurre il rischio, ma anche rallentare le fasi a basso rischio che l’agente AI gestisce già correttamente. Un guardrail più mirato, applicato nel punto in cui l’agente AI tende a recuperare un contesto inadeguato, scegliere lo strumento sbagliato o sovrastimare il proprio livello di certezza, può migliorare l’affidabilità senza trasformare l’intero workflow in una revisione manuale.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_agent-autonomy-and-guardrails_0":{"id":"text-53697368d9","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EL’ERRORE COMUNE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EUn errore comune consiste nel concedere a un agente AI un margine operativo eccessivo prima di comprendere bene il workflow. È preferibile iniziare con un task di cui siano visibili input, strumenti, approvazioni e punti critici, quindi ampliare l’autonomia man mano che il team identifica le attività eseguite in modo affidabile dall’agente AI e quelle che richiedono ancora vincoli.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_ai-agent-challenges-and-governance":{"id":"title-v2-10d71280f2","additionalClasses":"anchor-title anchor-title--ai-agent-challenges-and-governance","type":"heading2","lines":["Sfide e governance degli agenti AI"],":type":"snowflake-site/components/title-v2"},"text_ai-agent-challenges-and-governance_0":{"id":"text-3df8792aaa","text":"\u003Cp\u003ELa governance degli agenti AI deve tenere conto delle conseguenze, non solo degli output. Quando un sistema AI può recuperare dati, chiamare strumenti e agire nell’ambito di più workflow, gli errori non si limitano più a una risposta inesatta. L’agente AI potrebbe utilizzare un contesto non autorizzato, divulgare informazioni sensibili o eseguire un’azione con effetti a valle costosi o impossibili da annullare.\u003C/p\u003E\n\u003Ch3\u003EAccuratezza\u003C/h3\u003E\n\u003Cp\u003EL’accuratezza è una priorità fondamentale. Nei settori finanziario, ingegneristico, sanitario, della sicurezza e in altri ambiti ad alto rischio, l’agente AI deve recuperare il contesto corretto, utilizzare la definizione appropriata, applicare la policy pertinente e riconoscere quando il proprio livello di confidenza è troppo basso per procedere. La valutazione deve riflettere l’attività Ad esempio, un benchmark generico per LLM non indica se un agente AI applica correttamente le regole di rilevazione dei ricavi di un’organizzazione o gestisce l’escalation dell’avviso di sicurezza appropriato.\u003C/p\u003E\n\u003Ch3\u003EGovernance\u003C/h3\u003E\n\u003Cp\u003EAnche l’accesso governato ai dati è una priorità fondamentale. Gli agenti AI devono spesso operare su dati strutturati, documenti, audio, log, immagini, ticket e codice. Queste fonti adottano modelli di accesso diversi e la capacità di azione dell’agente AI non deve superare le autorizzazioni dell’utente. Un agente AI governato richiede la propagazione dell’identità, l’applicazione delle policy, controlli sulle query e audit trail che mostrino quali dati sono stati utilizzati.\u003C/p\u003E\n\u003Cp\u003EAllen sostiene tuttavia che le organizzazioni non debbano attendere di aver risolto ogni questione di governance prima di iniziare. “Devi definire le policy, orientarlo e governarlo. Ma non temere di procedere più rapidamente: fallo con un partner affidabile che comprenda gli aspetti di governance.”\u003C/p\u003E\n\u003Ch3\u003EFiducia e sicurezza\u003C/h3\u003E\n\u003Cp\u003EFiducia e sicurezza diventano più complesse quando gli agenti AI iniziano a utilizzare strumenti. Prompt injection, autorizzazioni eccessive, chiamate agli strumenti non sicure, data leakage e azioni di scrittura non autorizzate diventano rischi concreti. Il framework di sicurezza deve considerare sia il comportamento del modello sia i sistemi a cui può accedere.\u003C/p\u003E\n\u003Ch3\u003ECosti e latenza\u003C/h3\u003E\n\u003Cp\u003ECosti e latenza sono spesso il primo ostacolo operativo. Una singola interazione prompt-risposta potrebbe richiedere una chiamata al modello. Un workflow agentico articolato in più passaggi potrebbe includere pianificazione, recupero, chiamate agli strumenti, riflessione, un ulteriore passaggio di recupero e una risposta finale. Ogni passaggio aumenta costi e tempi. La riflessione, i cicli ReAct e l’orchestrazione multiagente sono utili quando l’attività li giustifica, ma comportano trade-off consapevoli tra costi e accuratezza.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-to-deploy-ai-agents":{"id":"title-v2-02cf5fa95c","additionalClasses":"anchor-title anchor-title--how-to-deploy-ai-agents","type":"heading2","lines":["Come implementare gli agenti AI"],":type":"snowflake-site/components/title-v2"},"text_how-to-deploy-ai-agents_0":{"id":"text-dc9fbf7ad9","text":"\u003Cp\u003EPrima che un agente AI possa operare in produzione, occorre definire i dati a cui può accedere, gli strumenti che può utilizzare, le azioni che può eseguire, le approvazioni necessarie e le evidenze richieste per verificare quanto accaduto. L’obiettivo non è semplicemente collegare un modello a più sistemi, ma creare un percorso di esecuzione governato che parta dalla richiesta dell’utente e arrivi all’azione dell’agente AI.\u003C/p\u003E\n\u003Ch3\u003EIniziare dall’accesso ai dati\u003C/h3\u003E\n\u003Cp\u003EPrima che un agente AI acceda ai dati di produzione, occorre definire cosa può leggere, cosa può scrivere e quale interfaccia deve utilizzare. In un’architettura governata, l’agente AI non deve disporre di un accesso diretto e senza restrizioni alle tabelle. Deve operare tramite livelli di query approvati, API, modelli semantici o sistemi di recupero che applichino identità, policy e contesto.\u003C/p\u003E\n\u003Ch3\u003EDefinire la governance a livello del control plane\u003C/h3\u003E\n\u003Cp\u003EIl piano di controllo deve applicare le autorizzazioni, instradare le chiamate agli strumenti, registrare le azioni nei log, gestire le approvazioni e interrompere i workflow che superano i limiti definiti. Se un agente AI può aggiornare un ticket, inviare un messaggio, modificare una pipeline o eseguire query su dati sensibili, il percorso di approvazione e audit deve essere chiaro prima dell’utilizzo in produzione.\u003C/p\u003E\n\u003Ch3\u003ELa valutazione precede il deployment\u003C/h3\u003E\n\u003Cp\u003EL’agente AI deve essere testato rispetto a benchmark specifici per l’attività, che riflettano il lavoro che svolgerà. Per un agente AI dedicato ai dati, i test potrebbero includere domande con definizioni ambigue delle metriche, tabelle obsolete, documenti discordanti, campi ad accesso limitato e casi limite noti. Le soglie di accuratezza devono essere commisurate al rischio del workflow. Un assistente interno per la gestione delle conoscenze può tollerare più incertezza rispetto a un agente AI per l’ingegneria che modifica l’infrastruttura o a un agente AI finanziario che fornisce informazioni per la reportistica.\u003C/p\u003E\n\u003Ch3\u003EIl monitoraggio continua dopo il deployment\u003C/h3\u003E\n\u003Cp\u003EOccorrono tracce che mostrino ogni passaggio del workflow dell’agente AI: contesto recuperato, chiamate agli strumenti, output intermedi, approvazioni, latenza, costi e azioni finali. Il rilevamento del drift, gli audit trail delle azioni e i percorsi di escalation human-in-the-loop consentono di capire quando cambia il comportamento dell’agente AI, quando peggiorano i suoi input o quando una decisione con un basso livello di confidenza richiede una revisione.\u003C/p\u003E\n\u003Cp\u003ENei sistemi di agenti AI in produzione serve inoltre un framework che mostri quali domande, strumenti, percorsi di recupero o passaggi del workflow causano errori, per poi verificare le correzioni proposte sia su casi mirati sia su suite di regressione. Snowflake \u003Ca href=\"https://www.snowflake.com/en/blog/engineering/optimize-snowflake-ai-systems-cocoevolve/\"\u003ECoCoEvolve ne è un esempio\u003C/a\u003E: il sistema racchiude un artefatto AI, propone modifiche, valuta se producono miglioramenti, conserva quelle efficaci e scarta quelle inefficaci.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_how-to-deploy-ai-agents_0":{"id":"text-76b67d824f","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003ECONSIGLIO RAPIDO\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EInizia da un workflow circoscritto e ripetibile, con fonti di dati, azioni consentite e punti di escalation chiaramente definiti. È più semplice ampliare un agente AI governato che integrare successivamente la governance in un agente AI che dispone già di un ampio accesso a più sistemi.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_the-future-of-ai-agents":{"id":"title-v2-473bec4953","additionalClasses":"anchor-title anchor-title--the-future-of-ai-agents","type":"heading2","lines":["Il futuro degli agenti AI"],":type":"snowflake-site/components/title-v2"},"text_the-future-of-ai-agents_0":{"id":"text-48c180019e","text":"\u003Cp\u003EIl futuro degli agenti AI sarà probabilmente determinato meno dai singoli miglioramenti dei modelli e più dai sistemi che forniscono il contesto, governano le azioni e coordinano il lavoro tra gli strumenti. Con il passaggio degli agenti AI dalle finestre di chat ai workflow operativi, le imprese avranno bisogno di architetture che rendano disponibili i dati corretti al momento opportuno, mantengano le autorizzazioni degli utenti e registrino quanto accade in ogni passaggio.\u003C/p\u003E\r\n\u003Cp\u003EIn ambito aziendale, questa architettura spesso richiede di avvicinare l’AI ai dati governati anziché copiare i dati in un ambiente AI separato. “Devi poter portare l’AI ai dati, senza spostarli per via di tutti gli aspetti di governance”, afferma Allen. “Mantenere i dati dove si trovano, e poi sfruttare l’AI per creare agenti che abbiano quel contesto.”\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_the-future-of-ai-agents_0":{"id":"quote-item-0bee7052f4","alignment":"left","layout":"simple","quoteSourceName":"William Allen","quoteSourceTitle":"Head of Product CoWork & Agents, Snowflake","showQuoteIcon":true,"quote":"Devi poter portare l'AI ai dati... mantenere i dati dove si trovano, e poi sfruttare l'AI per creare agenti che abbiano quel contesto.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_the-future-of-ai-agents_1":{"id":"text-6d37141365","text":"\u003Cp\u003EQuesta esigenza spiega anche la crescente importanza di protocolli come MCP. Gli agenti necessitano di un metodo coerente per individuare e utilizzare strumenti, fonti di dati e app, senza che ogni connessione dipenda da un’integrazione personalizzata. Nel novembre 2024, \u003Ca href=\"https://www.anthropic.com/news/donating-the-model-context-protocol-and-establishing-of-the-agentic-ai-foundation\" target=\"_blank\"\u003EAnthropic ha presentato MCP\u003C/a\u003E come standard open per collegare le app AI a sistemi esterni, per poi donarlo all’Agentic AI Foundation, istituita nell’ambito della Linux Foundation, nel dicembre 2025. Da allora, MCP ha superato l’ambito degli strumenti locali ed è \u003Ca href=\"https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/\" target=\"_blank\"\u003Eutilizzato in produzione da un numero crescente di organizzazioni\u003C/a\u003E.\u003C/p\u003E\n\u003Cp\u003ELa crescita di MCP indica l’emergere di un ecosistema agentico più connesso, ma la sola connettività non risolve le sfide aziendali. La fase successiva dipenderà anche dall’interoperabilità governata: agenti in grado di individuare gli strumenti, utilizzarli con l’identità corretta, rispettare le policy, preservare il contesto e trasferire le attività tra sistemi senza perdere la tracciabilità ai fini di audit.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_the-future-of-ai-agents_0":{"id":"text-a78e7c0fd2","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EIN SINTESI\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EIl futuro degli agenti aziendali non consiste semplicemente in una maggiore autonomia, ma in un’autonomia meglio controllata: agenti in grado di ragionare su dati, strumenti e workflow, mentre l’azienda mantiene il controllo su accessi, policy, approvazioni e responsabilità.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_the-future-of-ai-agents_2":{"id":"text-4c259f3e27","text":"\u003Cp\u003E\u003Csup\u003E1. Webinar Gartner, \u003Ca rel=\"noopener noreferrer\" href=\"https://www.gartner.com/en/webinar/784381/1775526-gartner-agentic-compass-for-ai-success-aligning-innovation-with-enterprise-needs\" target=\"_blank\"\u003EGartner Agentic Compass for AI Success: Aligning Innovation with Enterprise Needs\u003C/a\u003E | Gartner Webinars. 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